The agentic platform for Apache Spark
Most AI tools are blind to production. DataFlint enriches your Spark logs and serves them to your agents through a Spark MCP server, so they ship fixes that actually cut runtime and cost.
Works on any Spark platform: Databricks, EMR, Dataproc, Fabric, and open-source Spark.
Agentic Spark Copilot
Production-aware IDE copilot powered by enriched Spark logs and the Spark MCP server.
Cluster Agent
Right-sizes Spark clusters in real time using your enriched Spark logs.
Review Agent
Reviews every pull request with enriched production context from the Spark MCP server.
Fleet Observability
Company-wide Spark cost and performance dashboard built on enriched Spark logs.
How the agentic platform works
Enriched Spark logs become production context your agents can act on
1. Enrich
DataFlint compresses and enriches your raw Spark logs into deep production context
2. Serve via Spark MCP
A Spark MCP server exposes that context to your agents and AI tools
3. Act
Agents fix code, right-size clusters, review PRs, and rank cost savings
From Hours of Guesswork to Minutes of Precision
Most teams spend hours debugging Spark jobs with basic tools. DataFlint transforms this into a systematic, data-driven workflow.
Current State (Manual process)
- •4-8 hours to root cause issues manually
- •Guesswork to identify bottlenecks
- •Manual code fixes with context switching
- •No visibility into costs or optimization impact
DataFlint (AI-powered solution)
- •2-5 minutes with AI-powered analysis
- •Auto-detection with impact ranking
- •IDE integration with one-click fixes
- •Stage/team cost attribution with $ optimization ranking